We model one of the most complex systems: the human brain
Great number of neurons and enormous number of dendrons
Self organised network:
Random graph:
Iteratively update graph:
Each nodes activation value is updated according to the equation below
$$
\begin{equation*}
x^i_{n+1} = (1- \epsilon) \cdot f(x^j_n)+\frac{\epsilon}{M_i}\sum_{j \in B(i)}f(x^j_n)
\end{equation*}
$$
Where
$$f(x) = 1 - a x^2$$
Epsilon is the coupling parameter
a is a constant in the logistic map
plt.scatter(a,e)
plt.title("Parameter space")
plt.xlabel("a")
plt.ylabel("epsilon")
plt.grid()
plt.show()
init_notebook_mode(connected=True)
figure = build_fig()
iplot(figure, validate = False)
building figure